diff --git a/README.md b/README.md
index 638c5fd..bb7b4c0 100644
--- a/README.md
+++ b/README.md
@@ -246,6 +246,9 @@ python owl/run_deepseek_zh.py
 
 # Run with other OpenAI-compatible models
 python owl/run_openai_compatiable_model.py
+
+# Run with Ollama API
+python owl/run_ollama.py
 ```
 
 For a simpler version that only requires an LLM API key, you can try our minimal example:
diff --git a/README_zh.md b/README_zh.md
index e17758c..35c6af4 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -246,6 +246,9 @@ python owl/run_deepseek_zh.py
 
 # 使用其他 OpenAI 兼容模型运行
 python owl/run_openai_compatiable_model.py
+
+# 使用 Ollama API 运行
+python owl/run_ollama.py
 ```
 
 你可以通过修改 `run.py` 脚本来运行自己的任务：
diff --git a/owl/.env_template b/owl/.env_template
index 550f899..0d4cc01 100644
--- a/owl/.env_template
+++ b/owl/.env_template
@@ -1,8 +1,11 @@
 # MODEL & API (See https://github.com/camel-ai/camel/blob/master/camel/types/enums.py)
 
+# Ollama API
+OLLAMA_API_KEY=""
+
 # OPENAI API
-OPENAI_API_KEY = ""
-# OPENAI_API_BASE_URL = ""
+OPENAI_API_KEY= ""
+# OPENAI_API_BASE_URL=""
 
 # Qwen API (https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key)
 # QWEN_API_KEY=""
diff --git a/owl/app.py b/owl/app.py
index e214577..7c8d31e 100644
--- a/owl/app.py
+++ b/owl/app.py
@@ -40,6 +40,7 @@ SCRIPTS = {
     "Default": "run.py",
     "GAIA Roleplaying": "run_gaia_roleplaying.py",
     "OpenAI Compatible": "run_openai_compatiable_model.py",
+    "Ollama": "run_ollama.py",
 }
 
 # 脚本描述
@@ -51,6 +52,7 @@ SCRIPT_DESCRIPTIONS = {
     "Default": "默认OWL实现，使用OpenAI GPT-4o模型和全套工具",
     "GAIA Roleplaying": "GAIA基准测试实现，用于评估模型能力",
     "OpenAI Compatible": "使用兼容OpenAI API的第三方模型，支持自定义API端点",
+    "Ollama": "使用Ollama API",
 }
 
 # 环境变量分组
@@ -84,6 +86,13 @@ ENV_GROUPS = {
             "required": False,
             "help": "DeepSeek API密钥，用于访问DeepSeek模型。获取方式：https://platform.deepseek.com/api_keys",
         },
+        {
+  	    "name": "OLLAMA_API_KEY",
+	    "label": "Ollama API秘钥",
+	    "type": "password",
+	    "required": False,
+	    "help": "Ollama API秘钥，没啥用的",
+	},
     ],
     "搜索工具": [
         {
diff --git a/owl/run_ollama.py b/owl/run_ollama.py
new file mode 100644
index 0000000..ca0977c
--- /dev/null
+++ b/owl/run_ollama.py
@@ -0,0 +1,137 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# run_ollama.py by tj-scripts（https://github.com/tj-scripts）
+import os
+
+from dotenv import load_dotenv
+from camel.models import ModelFactory
+from camel.toolkits import (
+    CodeExecutionToolkit,
+    ExcelToolkit,
+    ImageAnalysisToolkit,
+    SearchToolkit,
+    WebToolkit,
+)
+from camel.types import ModelPlatformType
+
+from utils import OwlRolePlaying, run_society
+
+from camel.logger import set_log_level
+
+set_log_level(level="DEBUG")
+
+load_dotenv()
+
+
+def construct_society(question: str) -> OwlRolePlaying:
+    r"""Construct a society of agents based on the given question.
+
+    Args:
+        question (str): The task or question to be addressed by the society.
+
+    Returns:
+        OwlRolePlaying: A configured society of agents ready to address the question.
+    """
+
+    # Create models for different components
+    models = {
+        "user": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
+            model_type="qwen2.5:3b",
+            api_key=os.getenv("OLLAMA_API_KEY"),
+            url="http://localhost:11434/v1",
+            model_config_dict={"temperature": 0.8, "max_tokens": 4096},
+        ),
+        "assistant": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
+            model_type="qwen2.5:3b",
+            api_key=os.getenv("OLLAMA_API_KEY"),
+            url="http://localhost:11434/v1",
+            model_config_dict={"temperature": 0.2, "max_tokens": 4096},
+        ),
+        "web": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
+            model_type="llava:latest",
+            api_key=os.getenv("QWEN_API_KEY"),
+            url="http://localhost:11434/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 4096},
+        ),
+        "planning": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
+            model_type="qwen2.5:3b",
+            api_key=os.getenv("ollama"),
+            url="http://localhost:11434/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 4096},
+        ),
+        "image": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
+            model_type="llava:latest",
+            api_key=os.getenv("QWEN_API_KEY"),
+            url="http://localhost:11434/v1",
+            model_config_dict={"temperature": 0.4, "max_tokens": 4096},
+        ),
+    }
+
+    # Configure toolkits
+    tools = [
+        *WebToolkit(
+            headless=False,  # Set to True for headless mode (e.g., on remote servers)
+            web_agent_model=models["web"],
+            planning_agent_model=models["planning"],
+        ).get_tools(),
+        *CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
+        *ImageAnalysisToolkit(model=models["image"]).get_tools(),
+        SearchToolkit().search_duckduckgo,
+        #SearchToolkit().search_google,  # Comment this out if you don't have google search
+        SearchToolkit().search_wiki,
+        *ExcelToolkit().get_tools(),
+    ]
+
+    # Configure agent roles and parameters
+    user_agent_kwargs = {"model": models["user"]}
+    assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
+
+    # Configure task parameters
+    task_kwargs = {
+        "task_prompt": question,
+        "with_task_specify": False,
+    }
+
+    # Create and return the society
+    society = OwlRolePlaying(
+        **task_kwargs,
+        user_role_name="user",
+        user_agent_kwargs=user_agent_kwargs,
+        assistant_role_name="assistant",
+        assistant_agent_kwargs=assistant_agent_kwargs,
+    )
+
+    return society
+
+
+def main():
+    r"""Main function to run the OWL system with an example question."""
+    # Example research question
+    question = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."
+
+    # Construct and run the society
+    society = construct_society(question)
+    answer, chat_history, token_count = run_society(society)
+
+    # Output the result
+    print(f"\033[94mAnswer: {answer}\033[0m")
+
+
+if __name__ == "__main__":
+    main()